Zobrazeno 1 - 10
of 28 049
pro vyhledávání: '"Jiang,Li"'
Autor:
Liu, Chendong, Yang, Dapeng, Chen, Jiachen, Dai, Yiming, Jiang, Li, Xie, Shengquan, Liu, Hong
Fabric-based pneumatic exosuits have a broad application prospect due to their good human-machine interaction performance, but their structural design paradigm has not yet been finalized and requires in-depth research. This paper proposes the concept
Externí odkaz:
http://arxiv.org/abs/2410.11341
Recent approaches have shown that large-scale vision-language models such as CLIP can improve semantic segmentation performance. These methods typically aim for pixel-level vision-language alignment, but often rely on low resolution image features fr
Externí odkaz:
http://arxiv.org/abs/2407.21654
Chung's lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynomial diminishing step sizes. In this work, we develop a generalized ver
Externí odkaz:
http://arxiv.org/abs/2406.05637
Image copy-move is an operation that replaces one part of the image with another part of the same image, which can be used for illegal purposes due to the potential semantic changes. Recent studies have shown that keypoint-based algorithms achieved e
Externí odkaz:
http://arxiv.org/abs/2406.03271
Autor:
Jiang, Li, Wu, Yusen, Xiong, Junwu, Ruan, Jingqing, Ding, Yichuan, Guo, Qingpei, Wen, Zujie, Zhou, Jun, Deng, Xiaotie
Publikováno v:
COLM 2024
Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback. However, these datasets often demonstrate conflicting alignment o
Externí odkaz:
http://arxiv.org/abs/2405.11647
Autor:
Zhou, Hao, Hu, Chengming, Yuan, Ye, Cui, Yufei, Jin, Yili, Chen, Can, Wu, Haolun, Yuan, Dun, Jiang, Li, Wu, Di, Liu, Xue, Zhang, Charlie, Wang, Xianbin, Liu, Jiangchuan
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunit
Externí odkaz:
http://arxiv.org/abs/2405.10825
This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing la
Externí odkaz:
http://arxiv.org/abs/2404.07155
Autor:
Peng, Bohao, Wu, Xiaoyang, Jiang, Li, Chen, Yukang, Zhao, Hengshuang, Tian, Zhuotao, Jia, Jiaya
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3D semantic segmentation. However, sparse CNNs are still valuab
Externí odkaz:
http://arxiv.org/abs/2403.14418
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the position of other agents in a step-by-step manner where each predicted ti
Externí odkaz:
http://arxiv.org/abs/2403.13331
Autor:
Wang, Haiyang, Tang, Hao, Jiang, Li, Shi, Shaoshuai, Naeem, Muhammad Ferjad, Li, Hongsheng, Schiele, Bernt, Wang, Liwei
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large l
Externí odkaz:
http://arxiv.org/abs/2403.09394